Secondary batteries are repeatedly chargeable and dischargeable and thus are used as power source in a variety of fields.
For example, the secondary battery is used in a portable apparatus such as portable phone, laptop computer, digital camera, video camera, tablet computer, power tool, and so on that can be carried around in a user's hand.
Further, the secondary battery is used in a variety of electrically-driven power apparatus such as electric bicycle, electric motorcycle, electric vehicle, hybrid vehicle, electric ship, electric airplane, and so on.
Further, the area of using the secondary battery has gradually increased, from a power storage apparatus used for storing power generated with new renewable energy or surplus generated power to even uninterruptible power system for stable power feed to a variety of information communication apparatuses including server computer and communication base station.
The “state of charge” of the secondary battery represents a relative ratio of currently remaining capacity with reference to the capacity of a fully-charged battery, and it is expressed by percent or by numbers between 0 and 1.
Since the state of charge is indicative of an amount of energy remaining in the secondary battery, it is the essentially necessary parameter to control charging and discharging of the secondary battery. When the state of charge reaches 100%, charging has to stop, or when the state of charge reaches 0%, discharging has to stop. Further, the state of charge is utilized to control power of the secondary battery or estimate state of health of the secondary battery.
The state of charge may be estimated with ampere counting. The ampere counting determines the state of charge by integrating the charge current and discharge current over time. However, the ampere counting has a deteriorating accuracy as time elapses. This is because the error of a sensor that measures currents is accumulated over time.
Meanwhile, mathematical algorithms are utilized to estimate the state of charge of a battery. Most of such mathematical algorithms are derived from a circuit model. However, it is difficult to derive a perfect battery model that can accurately simulate the non-linear operational characteristics of the battery.
Recently, the Extended Kalman Filter (EKF) has been utilized as a tool to estimate the state of charge of the battery. The EKF is a probability statistical technique that estimates the state of an interior of a system by using measurable parameters. However, EKF has a gradually deteriorating accuracy as the secondary battery is degraded. This is because the parameters of the EKF change as the secondary battery is degraded.
For example, most EKF includes capacity and resistance of the secondary battery as parameters. While the capacity decreases and the resistance increases as the secondary battery is degraded, it is difficult to accurately update these changes.
Accordingly, it is necessary to adaptively update EKF according to the state of health of the secondary battery. The problem is that it is difficult to accurately estimate the state of health of the secondary battery while the secondary battery is in use. Further, the state of health is influenced by the environment in which the secondary battery is being used. For example, even when the parameters of EKF are updated according to the state of health, regardless of whether EKF is updated or not, the accuracy of EKF is not ensured if the secondary battery has been used in harsher than normal condition.